https://w3id.org/sciencelive/np/RAauQR3eY4NGILF2ei5a6YLbZjT7JDK_tQGYyl2R4DcMk
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sub:decrop-2025-cnn-scattering-stacking-study a <https://w3id.org/sciencelive/o/terms/FORRT-Replication-Study>,
<https://w3id.org/sciencelive/o/terms/Replication-Study>;
<http://www.w3.org/2000/01/rdf-schema#label> "CNN + scattering stacking on Decrop 2025's plankton splits";
<http://www.w3.org/2004/02/skos/core#related> <http://www.wikidata.org/entity/Q113009580>,
<http://www.wikidata.org/entity/Q17084460>, <http://www.wikidata.org/entity/Q184755>,
<http://www.wikidata.org/entity/Q2539>, <http://www.wikidata.org/entity/Q3604063>,
<http://www.wikidata.org/entity/Q98526763>;
<https://w3id.org/sciencelive/o/terms/hasDeviationDescription> "This is an extension, not a reproduction. Differences from Decrop et al. 2025: (1) we add a stacking meta-classifier on top of the CNN's softmax outputs; the original paper reports CNN-alone metrics. (2) We compute scattering features as additional input — the paper does not use scattering. (3) Reported metrics include mean rare-class recall (computed on the 13 classes with train-set size <200), which the paper does not report; aggregate top-1, top-5, micro/macro/weighted F1 are all from the same test.txt evaluation pipeline. Differences from Delouis et al. 2022 (FOSCAT method paper): we use FOSCAT's 2D scattering operator (scat_cov.funct) on flat RGB images, not the spherical HEALPix variant the paper develops; same library, different operator. As with chains #1–#4, FOSCAT was used via the annefou/FOSCAT@v0.1.0-cpu fork; that CPU patch has since been merged upstream and is included in foscat>=2026.4.1 on PyPI.";
<https://w3id.org/sciencelive/o/terms/hasDiscipline> <http://www.wikidata.org/entity/Q7173>;
<https://w3id.org/sciencelive/o/terms/hasMethodologyDescription> """Three-stage pipeline.
Stage 1 (01_scattering_features.py): compute multi-scale scattering features on RGB phytoplankton images via FOSCAT scat_cov.funct(NORIENT=8, KERNELSZ=3) at 64×64 pixels, producing 246-dimensional feature vectors per image. Features are computed on (a) a balanced training subset, (b) Decrop's released val.txt (33,829 images), and (c) Decrop's released test.txt (33,718 images).
Stage 2 (02_cnn_predictions.py): obtain CNN softmax probabilities for the same val and test images by running Decrop's pretrained EfficientNetV2-B0 via the authors' planktonclas package with 10-crop test-time augmentation. These predictions can equivalently be sourced from the upstream fiesta-decrop-reproduction repository (Zenodo 10.5281/zenodo.19701133), which is the FAIR-archived form of the same computation.
Stage 3 (03_stacking.py): fit a class-weighted scikit-learn LogisticRegression on the 246-dim scattering features (StandardScaler + class_weight='balanced') to obtain scattering-derived softmax probabilities; concatenate with CNN softmax probabilities to form 190-dim meta-features; train a second class-weighted LogisticRegression on Decrop's val split as the stacking meta-classifier; evaluate on Decrop's test split. Compute top-1, top-5, and per-class recall against the integer labels in test.txt. Mean rare-class recall is averaged over the 13 'rare' classes defined as those with fewer than 200 training-set examples in Decrop's train.txt. Comparator results: CNN alone, scattering+LR alone, naive 50/50 probability ensemble, and a hard-switch oracle ceiling (perfect choice between CNN and scattering predictions per image) for context.""";
<https://w3id.org/sciencelive/o/terms/hasScopeDescription> "This study tests whether multi-scale scattering features computed independently of the CNN can be combined with the CNN's softmax probabilities via a class-weighted stacking meta-classifier to lift mean rare-class recall on Decrop et al. 2025's exact 95-class phytoplankton classification benchmark, at acceptable cost to aggregate top-1 accuracy. The chain reuses Decrop's released dataset, pretrained CNN weights, and train.txt/val.txt/test.txt splits, asking whether the rare-species class-imbalance limitation acknowledged in the paper can be partially addressed by complementary feature representations.";
<https://w3id.org/sciencelive/o/terms/targetsClaim> <https://w3id.org/sciencelive/np/RAeI2cspabQBUMHiA94jOKjiBypsIFSPPDwH_f5l1sJu4/decrop-2025-cnn-scattering-stacking-claim> .
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<https://orcid.org/0000-0002-1784-2920> <http://xmlns.com/foaf/0.1/name> "Anne Fouilloux" .
this: dc:created "2026-04-26T19:36:55.873Z"^^xsd:dateTime;
dc:creator <https://orcid.org/0000-0002-1784-2920>;
dc:license <https://creativecommons.org/licenses/by/4.0/>;
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